Monday, July 20, 2026

Compute Cash Flow: Infrastructure Tends to Lead Other EcosystemRevenue Streams

Some observers rightly note that the “time to revenue” for high-performance computing “as a service” suppliers is crucial. At the moment, for example, some would point to service supplier investments to create the infrastructure.


Some might characterize the cash flow as benefitting chip suppliers at the expense of high-performance computing suppliers, and it is hard to argue with that observation. 


But infrastructure creation first; revenues second is a classic pattern in computing. That pattern has been seen in semiconductors, telecommunications, cloud computing, internet infrastructure, and now AI infrastructure.


Yes, there always is risk, as returns are not guaranteed. 


source: Yahoo Finance 


But it is simply a fact that many computing businesses require supply-leading infrastructure. 


Firms must build capacity before customers can fully exploit it, and that investment often involves high fixed costs, before applications demand and use cases can emerge. 


Principle

Explanation

High fixed costs

Infrastructure requires enormous upfront investment before any customers are served.

Low marginal costs

Once infrastructure exists, serving additional customers becomes relatively inexpensive.

Long construction cycles

Fabs, fiber networks, and data centers often require years to build.

Induced demand

Lower prices and better performance stimulate entirely new applications.

Network effects

Infrastructure becomes more valuable as additional users and complementary services appear.

Option value

Excess capacity allows entrepreneurs to create products that previously were impossible or uneconomic.


So will there be a revenue lag for high-performance computing utilities? Yes. 


Era

Infrastructure investment

Revenue followed later through...

Time lag

Mainframes (1960s)

IBM manufacturing plants, semiconductor production, service organizations

Enterprise computing adoption

Several years

Semiconductor fabs (1970s-present)

Multi-billion-dollar fabrication plants

PC, mobile, cloud, AI chip demand

2–5 years

Personal computers (1980s)

Intel processor fabs, Microsoft software ecosystem, OEM manufacturing

Mass PC adoption

3–5 years

Internet backbone (1990s)

Fiber optic networks, routers, submarine cables

E-commerce, search, streaming

5–10 years

Mobile broadband (2000s)

3G/4G towers, spectrum, fiber backhaul

Smartphone economy, app stores

3–8 years

Hyperscale cloud (2006-present)

Massive global data centers

Cloud software, SaaS, AI services

3–10 years

Content delivery networks

Global edge server deployments

Video streaming and cloud gaming

Several years

AI infrastructure (2023-present)

GPU clusters, AI factories, power generation

AI agents, enterprise AI, robotics, scientific computing

Still developing


But that is a documented pattern:

  • Chip fab investment precedes chip sales revenue

  • Data center investments preceded cloud computing as a service revenues

  • Railroads preceded nationwide commerce

  • Electric grids preceded widespread electrification

  • Fiber preceded streaming


So building GPU clusters will precede AI-native businesses.


But apparent overinvestment will always be a concern. 


Historically, such overinvestment often also occurs, whether that is long-haul fiber; dynamic random access memory, solar panels or data centers. 


The dangers of overinvestment also are real. But the necessity of infrastructure investment before applications, use cases and revenue can develop is a reality we have often seen. 


Saturday, July 18, 2026

For Every Public Policy There are Corresponding Private Interests

I learned a long time ago, as a student of public policy and then as a journalist, that “for every public policy there are corresponding private interests.”


So arguments about whether and how to regulate artificial intelligence in the context of content businesses always will be a combination of abstract public values, impact on culture, fairness, content quality or art and perceived personal economic interest.


Every major technological shift affecting content industries has altered

  • who creates value

  • who captures income

  • whose social status changes. 


Indeed, much of the economic value in content industries rests on scarcity, and AI threatens to create abundance. That might be a favorable outcome for content consumers, but might harm professional content producers. 


Traditional source of scarcity

Effect of AI

Skilled illustration

AI greatly expands supply

Copywriting

Near-zero marginal production cost

Translation

Instant multilingual capability

Stock photography

Synthetic images substitute for many uses

Voice acting

Synthetic voices compete in many applications

Video production

Increasing automation reduces labor inputs

Software documentation

AI drafts much routine material

Marketing content

Mass personalization becomes inexpensive


Whenever scarcity declines, prices usually follow. So the content industry advocates concern about AI "ethics” also are about protecting existing economic rents. 


That isn’t unusual. All professional associations, licensing requirements and unions, whatever their stated purpose (“safety,” often), are also about protecting economic rents.


Public policy concern

Corresponding private interest

Copyright protection

Licensing revenues

Artist consent

Control over monetization

Transparency

Ability to distinguish human work in the market

Watermarking

Preserve premium pricing for human-created work

Fair compensation

Maintain existing wage levels

Quality concerns

Preserve professional gatekeeping

Educational concerns

Preserve demand for traditional instruction

Safety regulation

Increase barriers to entry favoring incumbents

Cultural preservation

Preserve existing creative institutions


We can cite many content industry examples.


Technology

Incumbents defending existing value

Public argument

Private interest

Printing press

Scribes

Accuracy, religious authority

Preserve copying profession

Photography

Portrait painters

Artistic standards

Maintain commissions

Recorded music

Live performers

Artistic integrity

Preserve performance income

Radio

Newspapers

Media concentration

Advertising revenues

Television

Movie theaters

Cultural effects

Box office

Digital photography

Film manufacturers

Image quality

Film sales

MP3 files

Record labels

Copyright

Music distribution revenues

Streaming

Cable operators

Local programming

Subscription economics

Generative AI

Writers, artists, actors, publishers

Copyright, authenticity, quality

Employment, licensing, bargaining power


Also, AI threatens not only earnings but also professional identity, as creative professions provide:

  • expertise

  • prestige

  • cultural influence

  • reputation

  • gatekeeping authority

  • community standing. 


If AI enables non-experts to produce acceptable commercial work, professionals may lose status even before they lose substantial income.


The broader lesson from economic history is that technological debates are rarely contests between "public good" and "private greed." 


Instead, all public policies have corresponding private interests.


AI raises authentic questions about authorship, consent, cultural diversity, and market power. 


At the same time, it redistributes income, bargaining power, and professional status across the content ecosystem. 


That isn’t to deny the legitimacy of the issues raised. But neither does it make sense to deny the private financial interests also at stake. 


Good Outcomes Matter More Than Good Intentions

California voters will decide the fate of Proposition 40, a new wealth tax, in November 2026. The initiative would enact a one-time tax of five percent on the accumulated wealth of taxpayers and trusts with covered assets valued over $1 billion. 


As popular as such “soak the rich” policies might be in some quarters, governments imposing such wealth taxes have found highly mixed returns from the policies, the Organization for Economic Co-operation and Development says. 


Proponents of such taxes might tout the equity benefits. Critics might point out that high-net-worth individuals can, and do, simply move to avoid the taxes. 


But there are other issues, including the reality that wealth taxes often raise less revenue than expected, while imposing disproportionate economic and administrative costs. 


And such laws, where they have been imposed, are being repealed. In 1990, 12 OECD countries had such taxes. By 2017 there were just four OECD countries that continued to do so. 


The OECD report  notes that many countries repealed wealth taxes because they:

  • generated relatively little revenue

  • were expensive to administer

  • encouraged avoidance

  • were perceived as economically inefficient

  • often failed to achieve redistribution objectives as intended.


So migration of taxpayers is one of several objections to such taxes, which increasingly are possible when assets are internationally diversified, which increasingly is the case for such high-net-worth persons.


Migration was rarely the official reason cited by governments which repealed such taxes. The OECD notes that wealth taxes typically produced surprisingly small amounts of revenue compared with expectations.


OECD does not say such policies can never work. “For instance, a net wealth tax may have more limited distortive effects and be more justified as a way to enhance progressivity in countries where the taxation of personal capital income is comparatively low,” the report says. 


“Overall, the report concludes that from both an efficiency and equity perspective, there are limited arguments for having a net wealth tax in addition to broad-based personal capital income taxes and well-designed inheritance and gift taxes,” the authors note.


Considerations of equity benefits aside, one of the strongest criticisms of such taxes is their modest fiscal yield, as they generate small returns:

  • usually well under one percent of gross domestic product

  • generally a small share of total tax revenue;

  • often lower than expected because of exemptions, avoidance, valuation challenges, and migration.


The OECD repeatedly notes that low revenue was a major reason countries abandoned these taxes. In other words, the policies generally do not work. 


But the persistence of wealth taxes in countries such as Switzerland, Norway and Spain also shows that outcomes depend heavily on policy design, tax rates, exemptions, and the broader tax system, rather than on the mere existence of a wealth tax, the report suggests. 


It is unclear whether the California measure would produce meaningful revenue or not, but the key point is that, with all public policy, having good intentions is one thing. 


But that matters less than actual good outcomes. Some of us are likely doubtful the measure’s actual stated goals can be achieved, in practice. 


So it is in the category of actions we might say are mostly theatrical and symbolic. To the extent the measure would encourage migration out of the state, which would tend to lower tax receipts, the measure might even be counter productive. 


Compute Cash Flow: Infrastructure Tends to Lead Other EcosystemRevenue Streams

Some observers rightly note that the “time to revenue” for high-performance computing “as a service” suppliers is crucial. At the moment, fo...